Faddeev calculation of a K−ppK^- p p quasi-bound state

We report on the first genuinely three-body ${\bar K}NN - \pi \Sigma N$ coupled-channel Faddeev calculation in search for quasi-bound states in the $K^- p p$ system. The main absorptivity in the $K^- p$ subsystem is accounted for by fitting to $K^- p$ data near threshold. Our calculation yields one such quasi-bound state, with $I=1/2$, $J^{\pi}=0^-$, bound in the range $B \sim 55-70$ MeV, with a width of $\Gamma \sim 95-110$ MeV. These results differ substantially from previous estimates, and are at odds with the $K^- p p \to \Lambda p$ signal observed by the FINUDA collaboration.Comment: Minor editorial revision; version accepted for publication in Phys. Rev. Let

Latent Gaussian processes for distribution estimation of multivariate categorical data

Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.YG is supported by the Google European fellowship in Machine Learning.This is the final version of the article. It first appeared from Microtome Publishing via http://jmlr.org/proceedings/papers/v37/gala15.htm

Deep Bayesian Active Learning with Image Data

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).Alan Turing Institute Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Qualcom

Flow Games

In the traditional maximal-flow problem, the goal is to transfer maximum flow in a network by directing, in each vertex in the network, incoming flow into outgoing edges. While the problem has been extensively used in order to optimize the performance of networks in numerous application areas, it corresponds to a setting in which the authority has control on all vertices of the network. Today\u27s computing environment involves parties that should be considered adversarial. We introduce and study {em flow games}, which capture settings in which the authority can control only part of the vertices. In these games, the vertices are partitioned between two players: the authority and the environment. While the authority aims at maximizing the flow, the environment need not cooperate. We argue that flow games capture many modern settings, such as partially-controlled pipe or road systems or hybrid software-defined communication networks. We show that the problem of finding the maximal flow as well as an optimal strategy for the authority in an acyclic flow game is $Sigma_2^P$-complete, and is already $Sigma_2^P$-hard to approximate. We study variants of the game: a restriction to strategies that ensure no loss of flow, an extension to strategies that allow non-integral flows, which we prove to be stronger, and a dynamic setting in which a strategy for a vertex is chosen only once flow reaches the vertex. We discuss additional variants and their applications, and point to several interesting open problems

The Illusion of Multitasking and Its Positive Effect on Performance

Multitasking is pervasive. With technological advancements, the desire, ability, and often necessity to engage in multiple activities concurrently are paramount. Although multitasking refers to the simultaneous execution of multiple tasks, most activities that require active attention cannot actually be done simultaneously. Therefore, whether a certain activity is considered multitasking is often a matter of subjective perception. The current paper demonstrates the malleability of what people perceive as multitasking, showing that the same activity may or may not be construed as multitasking. Importantly, although engaging in multiple tasks may diminish performance, we find that, holding the activity constant, the mere perception of multitasking actually improves performance. Across 23 incentive-compatible studies, totaling 6,768 participants, we find that those who perceived an activity as multitasking were more engaged, and consequently outperformed those who perceived that same activity as single-tasking